As computer technology advances, computing systems have undertaken the management and processing of larger data systems. With data systems ranging from massive standalone databases to vast distributed networks, oftentimes the limiting factor in analyzing the state of a given system rests not with computing resources, but with the human operator. Specifically, though the computing system may aggregate vast quantities of data in near real-time, in many cases, a human being must visualize the compilation of data to draw effective conclusions from the visualization. Yet, the ability of the end user to digest compiled information varies inversely with the amount of data presented to the end user. Where the amount of compiled data becomes excessive, it can be nearly impossible for a human being to adequately analyze the data.
In an effort to address the foregoing difficulties, tree-map visualization methods have been developed. Initially proposed by Brian Johnson and Ben Shneiderman in the paper, Johnson et al., Tree-Maps: A Space-Filling Approach to the Visualization of Hierarchical Information Structures, Dept. of Computer Science & Human-Interaction Laboratory (University of Maryland June 1991), tree-map visualization techniques map “hierarchical information to a rectangular 2-D display in a space-filling manner” in which the entirety of a designated display space is utilized. Additionally, “[i]nteractive control allows users to specify the presentation of both structural (depth bounds, etc.) and content (display properties such as color mappings) information.” Tree-map visualization techniques can be compared in a contrasting manner to traditional static methods of displaying hierarchically structured information.
According to conventional static methods, a substantial portion of hierarchical information can be hidden from user view to accommodate the view of the hierarchy itself. Alternatively, the entire hierarchy can be visually represented, albeit vast amounts of display space can be obscured, hence wasted, simply to accommodate the structure without regard to the hierarchical data in the hierarchy itself. In the tree-map visualization technique, however, sections of the hierarchy containing more important information can be allocated more display space while portions of the hierarchy which are deemed less important to the specific task at hand can be allocated less space. More particularly, in operation, tree-maps partition the display space into a collection of rectangular bounding boxes representing the tree structure. The drawing of nodes within the bounding boxes can be entirely dependent on the content of the nodes, and can be interactively controlled. Since the display space size is user controlled, the drawing size of each node varies inversely with the size of the tree, for instance the number of nodes. Thus, trees having many nodes can be displayed and manipulated in a fixed display space, yet still be visible even when dealing with 1 million objects.
FIG. 1 illustrates a conventional tree map display 10. As seen in FIG. 1, a 10 by 10 display grid is filled with bounding boxes 12 through 68 representing the display of a data set containing twenty-nine entries. Data values associated with the twenty-nine entries establish the size of the bounding boxes and the color of the box, as represented by the different cross-hatch patterns illustrated in FIG. 1. Thus, a first data value may establish the size of the bounding box, for example, market capitalization if the data set represents different stocks, and a second data value may establish the color of the bounding box, for example, the change in stock price. Thus, in the example illustrated in FIG. 1, the tree map display 10 is created from the data set of Table 1 below.
TABLE 1Exemplary DataBounding BoxFirst Data ValueSecond Data Value12201 (no cross-hatch)14124 (diagonal left-right)1684 (diagonal left-right)1881 (no cross-hatch)2084 (diagonal left-right)2261 (no cross-hatch)2463 (diagonal right-left)2643 (diagonal right-left)2843 (diagonal right-left)3042 (vertical cross-hatch)3221 (no cross-hatch)3414 (diagonal left-right)3611 (no cross-hatch)3811 (no cross-hatch)4013 (diagonal right-left)4211 (no cross-hatch)4414 (diagonal left-right)4612 (vertical cross-hatch)4811 (no cross-hatch)5014 (diagonal left-right)5212 (vertical cross-hatch)5413 (diagonal right-left)5611 (no cross-hatch)5811 (no cross-hatch)6014 (diagonal left-right)6213 (diagonal right-left)6413 (diagonal fight-left)6611 (no cross-hatch)6812 (vertical cross-hatch)
A further example of the use of a tree map visualization is provided by Fidelity Investments' map of the stock market (which may be found at activequote.fidelity.com/rtmews/market_map.phtml). In the Fidelity market map, the market is divided into sectors and the sectors are populated with bounding boxes for individual stocks. The size of the bounding boxes is based on the market capitalization of the stock and the color of the boxes are based on the price activity of the stock.